{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-yiyanghkust--finbert-tone","slug":"yiyanghkust--finbert-tone","name":"finbert-tone","type":"model","url":"https://huggingface.co/yiyanghkust/finbert-tone","page_url":"https://unfragile.ai/yiyanghkust--finbert-tone","categories":["data-analysis"],"tags":["transformers","pytorch","tf","text-classification","financial-sentiment-analysis","sentiment-analysis","en","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-yiyanghkust--finbert-tone__cap_0","uri":"capability://data.processing.analysis.financial.sentiment.classification.with.domain.adaptation","name":"financial-sentiment-classification-with-domain-adaptation","description":"Classifies text into positive, negative, or neutral sentiment categories using a BERT-based transformer fine-tuned on financial domain corpora. The model applies domain-adaptive pretraining on financial documents before task-specific fine-tuning, enabling it to recognize financial terminology and context-specific sentiment signals (e.g., 'dilution' as negative, 'synergy' as positive) that generic sentiment models miss. Inference runs via HuggingFace Transformers library with tokenization, embedding generation, and classification head prediction in a single forward pass.","intents":["Analyze sentiment of earnings call transcripts, financial news, or SEC filings to track market sentiment","Classify investor sentiment in earnings reports or analyst notes for portfolio decision-making","Batch-process financial documents to extract sentiment signals for quantitative trading strategies","Monitor real-time financial news feeds for sentiment shifts in specific companies or sectors"],"best_for":["quantitative analysts and fintech teams building sentiment-driven trading signals","financial data engineers processing large corpora of earnings calls, news, or regulatory filings","compliance teams analyzing tone in internal communications or external disclosures"],"limitations":["Input limited to ~512 tokens due to BERT's context window; longer documents require chunking and aggregation strategies","Fine-tuned on English financial text only; non-English or domain-shifted financial language (e.g., crypto, emerging markets) may degrade accuracy","Sentiment classes are coarse-grained (positive/negative/neutral); no intensity scoring or multi-aspect sentiment (e.g., sentiment toward specific entities within text)","No built-in handling of sarcasm, negation scope, or complex linguistic phenomena common in financial communication","Inference latency ~50-200ms per sample on CPU; GPU required for high-throughput batch processing (>1000 docs/sec)"],"requires":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.4+","HuggingFace Transformers library (>=4.0)","~500MB disk space for model weights","4GB+ RAM for inference; GPU (CUDA 11.0+) recommended for production throughput"],"input_types":["raw text (string)","pre-tokenized text (list of tokens)","text files (plain text, CSV, JSON with text field)"],"output_types":["classification labels (positive/negative/neutral)","confidence scores per class (logits or probabilities)","structured JSON with label and confidence"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-yiyanghkust--finbert-tone__cap_1","uri":"capability://data.processing.analysis.batch.inference.with.huggingface.pipeline.abstraction","name":"batch-inference-with-huggingface-pipeline-abstraction","description":"Provides a high-level pipeline abstraction via HuggingFace Transformers that handles tokenization, batching, padding, and post-processing in a single API call. Internally, the pipeline manages device placement (CPU/GPU), dynamic batching, and attention mask generation, abstracting away low-level tensor operations. Supports both eager execution and optimized inference modes (e.g., ONNX, quantization) for production deployment.","intents":["Process hundreds or thousands of financial documents in parallel without writing custom batching logic","Deploy the model to cloud endpoints (Azure, AWS, HuggingFace Inference API) with minimal code changes","Integrate sentiment classification into data pipelines (Spark, Airflow, Pandas) with a simple function call"],"best_for":["data engineers building ETL pipelines for financial sentiment analysis","MLOps teams deploying models to managed inference endpoints","teams without deep ML expertise who need production-ready inference without custom optimization"],"limitations":["Pipeline abstraction adds ~10-20ms overhead per batch due to Python-level orchestration; not suitable for sub-10ms latency requirements","Batching is synchronous; no async/streaming support for real-time event processing","Dynamic batching may cause memory spikes if batch size is not tuned; requires manual profiling for optimal throughput","No built-in caching or deduplication; identical texts are re-processed if submitted multiple times"],"requires":["HuggingFace Transformers >=4.0","PyTorch or TensorFlow installed","Python 3.7+"],"input_types":["list of strings (texts)","pandas DataFrame with text column","generator/iterator of texts for streaming"],"output_types":["list of dicts with 'label' and 'score' keys","pandas Series or DataFrame with predictions"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-yiyanghkust--finbert-tone__cap_2","uri":"capability://data.processing.analysis.model.quantization.and.optimization.for.edge.deployment","name":"model-quantization-and-optimization-for-edge-deployment","description":"Supports quantization (INT8, FP16) and distillation-compatible architectures, enabling deployment to resource-constrained environments (mobile, edge devices, serverless functions). The model can be exported to ONNX format for cross-platform inference, and quantized versions reduce model size by 4x (from ~500MB to ~125MB) with <2% accuracy loss. Inference latency improves 2-3x on CPU with quantization, making real-time processing feasible on edge hardware.","intents":["Deploy sentiment analysis to mobile apps or browser-based tools without cloud dependency","Run inference on AWS Lambda or similar serverless platforms with strict memory/latency constraints","Reduce model serving costs by running quantized versions on cheaper CPU-only infrastructure"],"best_for":["mobile and edge ML teams building offline-capable financial apps","cost-conscious teams deploying high-volume inference on serverless or edge infrastructure","organizations with data privacy requirements that prohibit cloud inference"],"limitations":["Quantization introduces 1-3% accuracy degradation on financial sentiment; requires validation on domain-specific test sets","ONNX export requires additional tooling (onnx, onnxruntime); not all HuggingFace features are ONNX-compatible","Edge deployment requires custom integration per platform (iOS CoreML, Android TensorFlow Lite, WebAssembly); no unified deployment framework","Quantized models lose fine-grained probability scores; output is coarser (e.g., only top-1 label, no confidence distribution)"],"requires":["ONNX Runtime or TensorFlow Lite for inference","Quantization tools: torch.quantization (PyTorch) or TensorFlow Lite Converter","Platform-specific SDKs (CoreML Tools for iOS, TensorFlow Lite for Android, WASM runtime for browser)"],"input_types":["text (string)","pre-tokenized integers (for ONNX/ONNX Runtime)"],"output_types":["classification label (positive/negative/neutral)","logits (quantized to INT8 or FP16)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-yiyanghkust--finbert-tone__cap_3","uri":"capability://automation.workflow.multi.framework.model.export.and.serving","name":"multi-framework-model-export-and-serving","description":"Model is compatible with PyTorch, TensorFlow, and ONNX inference runtimes, enabling deployment across diverse serving infrastructure (TorchServe, TensorFlow Serving, ONNX Runtime, HuggingFace Inference API, Azure ML, AWS SageMaker). The HuggingFace model hub provides pre-built Docker containers and deployment templates for major cloud platforms, abstracting infrastructure-specific configuration. Supports both synchronous (REST API) and asynchronous (batch) serving patterns.","intents":["Deploy the same model to multiple cloud providers (AWS, Azure, GCP) without retraining or format conversion","Integrate sentiment analysis into existing ML serving infrastructure (Kubernetes, Docker, serverless) with minimal changes","Scale inference horizontally across multiple replicas with load balancing and auto-scaling policies"],"best_for":["MLOps and platform engineering teams managing multi-cloud or hybrid deployments","teams with existing ML serving infrastructure (Kubernetes, TorchServe) seeking to add sentiment analysis","organizations requiring vendor-agnostic model deployment for cost optimization or compliance"],"limitations":["Framework-specific optimizations (e.g., TensorFlow's XLA, PyTorch's TorchScript) are not automatically applied; requires manual tuning per framework","ONNX export may lose some HuggingFace-specific features (e.g., custom preprocessing, attention visualization); requires testing","Serving infrastructure (TorchServe, TensorFlow Serving) adds operational complexity; requires monitoring, versioning, and rollback strategies","Cold start latency on serverless platforms (AWS Lambda, Azure Functions) can be 5-30 seconds due to model loading; requires warm-up or provisioned concurrency"],"requires":["PyTorch 1.9+ OR TensorFlow 2.4+ OR ONNX Runtime 1.10+","Serving framework: TorchServe, TensorFlow Serving, ONNX Runtime Server, or cloud-managed endpoints","Container runtime (Docker) for on-premises or Kubernetes deployment","Cloud SDK (AWS CLI, Azure CLI, gcloud) for cloud deployment"],"input_types":["REST API JSON payload with 'text' field","batch inference requests (CSV, Parquet, JSON Lines)","streaming events (Kafka, Kinesis)"],"output_types":["REST API JSON response with label and confidence","batch prediction files (CSV, Parquet)","streaming predictions (Kafka, Kinesis)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-yiyanghkust--finbert-tone__cap_4","uri":"capability://data.processing.analysis.transfer.learning.and.fine.tuning.on.custom.financial.data","name":"transfer-learning-and-fine-tuning-on-custom-financial-data","description":"Model weights are available for transfer learning; users can fine-tune the pretrained financial BERT on custom labeled financial text (e.g., internal earnings calls, proprietary news feeds, domain-specific terminology). Fine-tuning leverages the model's existing financial vocabulary and attention patterns, requiring only 100-1000 labeled examples to adapt to new domains (vs 10,000+ for training from scratch). Training is efficient via gradient checkpointing and mixed-precision (FP16) training, reducing memory and compute requirements by 50-70%.","intents":["Adapt the model to company-specific sentiment signals (e.g., internal jargon, proprietary metrics) with minimal labeled data","Fine-tune on multi-lingual financial text or domain-shifted data (e.g., crypto sentiment, emerging market news)","Build ensemble models by fine-tuning multiple copies on different financial sub-domains (equity, fixed income, derivatives)"],"best_for":["financial institutions with proprietary labeled datasets seeking domain-specific sentiment models","research teams exploring financial NLP with limited annotation budgets","teams building specialized sentiment models for niche financial domains (crypto, commodities, ESG)"],"limitations":["Fine-tuning requires labeled data; weak supervision or zero-shot approaches are not supported","Overfitting risk on small datasets (<500 examples); requires careful hyperparameter tuning and validation strategies","Fine-tuning introduces model drift; requires continuous monitoring and retraining as financial language evolves","No built-in active learning or data annotation tools; requires external labeling infrastructure","Fine-tuned models are not automatically compatible with quantization or ONNX export; requires re-export and validation"],"requires":["PyTorch 1.9+ or TensorFlow 2.4+","HuggingFace Transformers >=4.0 and Datasets library","GPU with 8GB+ VRAM for efficient fine-tuning (16GB+ recommended for batch size >16)","Labeled training data: 100-1000 examples minimum for domain adaptation, 5000+ for significant distribution shift"],"input_types":["CSV/JSON with 'text' and 'label' columns","HuggingFace Datasets format","pandas DataFrame"],"output_types":["fine-tuned model weights (PyTorch .bin or TensorFlow SavedModel)","training metrics (loss, accuracy, F1 per epoch)","evaluation report on validation set"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":45,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.4+","HuggingFace Transformers library (>=4.0)","~500MB disk space for model weights","4GB+ RAM for inference; GPU (CUDA 11.0+) recommended for production throughput","HuggingFace Transformers >=4.0","PyTorch or TensorFlow installed","ONNX Runtime or TensorFlow Lite for inference","Quantization tools: torch.quantization (PyTorch) or TensorFlow Lite Converter","Platform-specific SDKs (CoreML Tools for iOS, TensorFlow Lite for Android, WASM runtime for browser)"],"failure_modes":["Input limited to ~512 tokens due to BERT's context window; longer documents require chunking and aggregation strategies","Fine-tuned on English financial text only; non-English or domain-shifted financial language (e.g., crypto, emerging markets) may degrade accuracy","Sentiment classes are coarse-grained (positive/negative/neutral); no intensity scoring or multi-aspect sentiment (e.g., sentiment toward specific entities within text)","No built-in handling of sarcasm, negation scope, or complex linguistic phenomena common in financial communication","Inference latency ~50-200ms per sample on CPU; GPU required for high-throughput batch processing (>1000 docs/sec)","Pipeline abstraction adds ~10-20ms overhead per batch due to Python-level orchestration; not suitable for sub-10ms latency requirements","Batching is synchronous; no async/streaming support for real-time event processing","Dynamic batching may cause memory spikes if batch size is not tuned; requires manual profiling for optimal throughput","No built-in caching or deduplication; identical texts are re-processed if submitted multiple times","Quantization introduces 1-3% accuracy degradation on financial sentiment; requires validation on domain-specific test sets","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7179590157032093,"quality":0.2,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.766Z","last_scraped_at":"2026-05-03T14:23:00.976Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":945210,"model_likes":217}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=yiyanghkust--finbert-tone","compare_url":"https://unfragile.ai/compare?artifact=yiyanghkust--finbert-tone"}},"signature":"syMFl22Yg5F5YBrU78feH6dIxmUGnbVmAK0I4iHbgZqKafpd6mQXoSOc59PppVJ1+ZnX7iF4Q3/KWNyb2Q9UAg==","signedAt":"2026-06-22T10:49:44.447Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/yiyanghkust--finbert-tone","artifact":"https://unfragile.ai/yiyanghkust--finbert-tone","verify":"https://unfragile.ai/api/v1/verify?slug=yiyanghkust--finbert-tone","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}